Publication classification prediction via citation attention fusion based on dynamic relations

2021 ◽  
pp. 108056
Author(s):  
Caixia Jing ◽  
Liqing Qiu ◽  
Xiangbo Tian ◽  
Tingyu Hao
Keyword(s):  
Dynamic Relations ◽  
Classification Prediction

scholarly journals What matters? The role of values in transformations toward sustainability: a case study of coffee production in Burundi

2021 ◽  
Author(s):  
Milda Nordbø Rosenberg
Keyword(s):  
Vital Role ◽  
Ecological Systems ◽  
New Materialism ◽  
Discursive Practices ◽  
Coffee Production ◽  
Ethnographic Fieldwork ◽  
Dynamic Relations ◽  
Participatory Observation

AbstractThis paper examines the role of values in transformations toward sustainability. Values, generally defined as what people deem to matter, are increasingly gaining interest in and outside of academia. For example, sustainability aligns with specific values such as dignity, equality, safety, and harmony for people and nature. However, current approaches to values are mind-matter dualistic, and therefore failing to honor the inherently dynamic relations of socio-ecological systems. Drawing on new materialism, I explore values as part of the relations that make this world and propose to consider values as material-discursive practices. Ethnographic fieldwork was done in 2017 with coffee producers in Burundi who aimed to transform production by caring for the coffee and people that grow it. Based on interviews and participatory observation, I present how values were integral to transforming the relational aspects of coffee production. In this study, values of togetherness, care, dignity, and faith were dominant and were found to reconfigure the socio-ecological system of coffee production. I argue that values are inseparable from, and hence co-productive of, the material world that we experience and play a vital role in sustainability transformations.

Stability Prediction of Surrounding Rocks and Optimum Design of Roof-Bolt Parameters in Deep Roadway

2013 ◽  
Vol 353-356 ◽  
pp. 436-439
Author(s):  
De Sen Kong ◽  
Yong Po Chen
Keyword(s):  
Prediction Method ◽  
Simulation Method ◽  
Complex Stress ◽  
Roof Bolt ◽  
Deformation And Failure ◽  
Long Run ◽  
Research Findings ◽  
Stress Environment ◽  
Classification Prediction ◽  
The Stability

In order to forecast the stability of deep roadway and optimize the parameters of bolts, the complex stress environment and the multivariate surrounding rocks characteristics of deep roadway were analyzed. Then the classification prediction method and the numerical simulation method were simultaneously used to analysis the stability of surrounding rocks. Furthermore, the supporting parameters of bolts were also designed optimally. It was shown that the characteristics of stress distribution, deformation and failure zone of surrounding rocks are not ideal. So it is necessary to optimize the supporting parameters of deep roadway. All these research findings will provide the theory basis for bolts of deep roadway and will ensure the optimization of bolts and the stability of deep roadway in the long run.

Classification prediction analysis of RSSI parameter in hard switching process for FSO/RF systems

Measurement ◽  
2018 ◽  
Vol 116 ◽  
pp. 602-610 ◽  
Author(s):  
Ján Tóth ◽  
Ľuboš Ovseník ◽  
Ján Turán ◽  
Linus Michaeli ◽  
Michal Márton
Keyword(s):  
Switching Process ◽  
Prediction Analysis ◽  
Rf Systems ◽  
Classification Prediction

scholarly journals Dynamic Relations of Consumer Prices: A Case Study of Recent Effects on the Japanese Headline CPI

2016 ◽  
Vol 3 (2) ◽  
pp. 28
Author(s):  
Chikashi Tsuji
Keyword(s):  
Impulse Response ◽  
Statistical Significance ◽  
Consumer Price Index ◽  
Variable Coefficients ◽  
Vector Error Correction ◽  
Vector Error ◽  
Consumer Prices ◽  
Dynamic Relations ◽  
Vector Error Correction Models

<p>This study attempts to empirically examine the relations between the headline consumer price index (CPI) and several other CPIs in Japan by applying the vector error correction models (VECMs). Our investigations derive the following interesting findings. First, we reveal that as to our four combinations of the CPIs tested in this paper, 1) all variable coefficients in the cointegrating equations are statistically significant in our VECM models and the statistical significance is very strong. Thus, we understand that our four bivariate combinations of the CPIs tested in this paper are all strongly cointegrated and the VECM approach is very effective to capture the time-series effects of the categorized CPIs on the Japanese headline CPI. Further, we also find that 2) as far as judging by the results of our impulse response analyses, for the period from May 2011 to June 2015, the headline CPI for Japan is weakly or little affected by the CPI of energy and the CPI of food for Japan. We further clarify that 3) according to the results of our impulse response analyses, the Japanese headline CPI is positively affected by both the CPI of utilities for Japan and the CPI of transportation and communication expenses for Japan.</p>

scholarly journals A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

2017 ◽  
Vol 19 (3) ◽  
pp. 1790-1821 ◽  
Author(s):  
Nicola Bui ◽  
Matteo Cesana ◽  
S. Amir Hosseini ◽  
Qi Liao ◽  
Ilaria Malanchini ◽  
...  
Keyword(s):  
Optimization Techniques ◽  
Mobile Networking ◽  
Classification Prediction

scholarly journals Data-mining Techniques for Image-based Plant Phenotypic Traits Identification and Classification

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Md. Matiur Rahaman ◽  
Md. Asif Ahsan ◽  
Ming Chen
Keyword(s):  
Data Mining ◽  
Agricultural Development ◽  
Computational Time ◽  
Support Vector ◽  
Phenotypic Traits ◽  
Linear Discriminant ◽  
Plant Phenomics ◽  
Classification Prediction ◽  
Linear Svm ◽  
Integrated Or

AbstractStatistical data-mining (DM) and machine learning (ML) are promising tools to assist in the analysis of complex dataset. In recent decades, in the precision of agricultural development, plant phenomics study is crucial for high-throughput phenotyping of local crop cultivars. Therefore, integrated or a new analytical approach is needed to deal with these phenomics data. We proposed a statistical framework for the analysis of phenomics data by integrating DM and ML methods. The most popular supervised ML methods; Linear Discriminant Analysis (LDA), Random Forest (RF), Support Vector Machine with linear (SVM-l) and radial basis (SVM-r) kernel are used for classification/prediction plant status (stress/non-stress) to validate our proposed approach. Several simulated and real plant phenotype datasets were analyzed. The results described the significant contribution of the features (selected by our proposed approach) throughout the analysis. In this study, we showed that the proposed approach removed phenotype data analysis complexity, reduced computational time of ML algorithms, and increased prediction accuracy.

scholarly journals Text Classification of Public Feedbacks using Convolutional Neural Network Based on Differential Evolution Algorithm

2019 ◽  
Vol 14 (1) ◽  
pp. 124-134 ◽  
Author(s):  
Shuai Zhang ◽  
Yong Chen ◽  
Xiaoling Huang ◽  
Yishuai Cai
Keyword(s):  
Neural Network ◽  
Differential Evolution ◽  
Convolutional Neural Network ◽  
Text Classification ◽  
Differential Evolution Algorithm ◽  
Classification Performance ◽  
Classification Model ◽  
Evolution Algorithm ◽  
Classification Prediction

Online feedback is an effective way of communication between government departments and citizens. However, the daily high number of public feedbacks has increased the burden on government administrators. The deep learning method is good at automatically analyzing and extracting deep features of data, and then improving the accuracy of classification prediction. In this study, we aim to use the text classification model to achieve the automatic classification of public feedbacks to reduce the work pressure of administrator. In particular, a convolutional neural network model combined with word embedding and optimized by differential evolution algorithm is adopted. At the same time, we compared it with seven common text classification models, and the results show that the model we explored has good classification performance under different evaluation metrics, including accuracy, precision, recall, and F1-score.

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